The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

AI spending levels 

Artificial Intelligence (AI) is revolutionising various industries, and the financial services sector is no exception. 

This surge in investment is driven by AI’s potential to transform various aspects of financial services, from customer experiences to back-office operations.

Current deployment of AI in Financial Services 

While much of the recent hype has centred around consumer-facing AI applications, it’s clear from our work and client conversations that the most significant AI operations are happening behind the scenes. 

Middle and back-office functions, traditionally characterised by manual processes and data-intensive tasks, are the primary targets for AI transformation.

AI is being used right now to streamline operations, enhance efficiency, mitigate risks, and reduce costs. For example, through process automation, risk management optimisation, and improving compliance processes. 

Where AI yields significant benefits

Various middle and back office functions within financial institutions are already reaping the rewards from AI:

• Tech and Coding: AI enhances engineering capacity by automating code generation, creating synthetic data, and optimising algorithms.
• HR Training and Onboarding: AI-powered tools streamline the entire process.
• Risk and Compliance: AI models improve fraud detection and financial crime investigations, ensuring compliance with regulatory requirements.
• Operations and Process Documentation: AI solutions provide quick access to specific tasks, improving operational efficiency and reducing human errors.
• Commercial and Marketing: AI optimises marketing campaigns by personalising content and enhancing customer engagement.

Front office challenges for AI adoption

Despite the immense potential, the pathway to widespread AI adoption for customer facing AI presents huge challenges.

“The reality is nowhere close to the hype” – Senior Executive

Significant risks arise from data sovereignty, client confidentiality, limited control boundaries, intricate interactions, and AI hallucinations. As such, a  prudent implementation strategy is essential to ensure AI’s safe and effective use on the front line.

How organisations deploy AI 

The organisational structure for AI deployment can significantly impact its effectiveness. Financial institutions typically adopt either a centralised or federated approach:

• Centralised Approach: Core infrastructure, use case aggregation, and seed funding are managed centrally. This ensures reliability and consistency but may create bottlenecks and data-access challenges.
• Federated Approach: Individual teams have autonomy to develop and implement AI solutions based on their specific needs. This approach fosters agility and specialised expertise but may lead to fragmentation and scalability issues.
• Hub and Spoke approach: Some digitally mature institutions are exploring hub-and-spoke models that combine elements of both.

Looking ahead 

In our latest report we build on the above to reveal recommendations for greater AI adoption.

Specifically, we share:

• An in-depth look at the current landscape, challenges and potential of AI 
• Specific use cases currently being deployed in financial services
• A prioritisation framework for leadership to help begin the AI journey and make decisions on the use cases to pursue

DOWNLOAD THE FULL REPORT